MEAN: A Multi-Element Attention Based Network for Scene Text Recognition

Ruijie Yan, Liangrui Peng, Shanyu Xiao, Gang Yao, Jaesik Min

Responsive image

Auto-TLDR; Multi-element Attention Network for Scene Text Recognition

Slides Poster

Scene text recognition is a challenging problem due to the wide variances in content, style, orientation, and image quality of text instances in natural scene images. To learn the intrinsic representation of scene texts, a novel multi-element attention (MEA) mechanism is proposed to exploit geometric structures from local to global levels in the feature map extracted from a scene text image. The MEA mechanism is a generalized form of self-attention technique with the incorporation of graph structure modeling. The elements in feature maps are taken as the nodes of an undirected graph, and three kinds of adjacency matrices are introduced to aggregating information at local, neighborhood and global levels before calculating the attention weights. If only the local adjacency matrix is used, the MEA mechanism degenerates to a self-attention form. A multi-element attention network (MEAN) is implemented which includes a CNN for feature extraction, an encoder with MEA mechanism and a decoder for predicting text codes. Orientation positional encoding information is further added to the feature map output by the CNN, and a feature sequence as the encoder's input is obtained by element-level decomposition of the feature map. Experimental results show that MEAN has achieved state-of-the-art or competitive performance on public English scene text datasets. Further experiments and analyses conducted on both English and Chinese scene text datasets show that MEAN can handle horizontal, vertical, and irregular scene text samples.

Similar papers

A Multi-Head Self-Relation Network for Scene Text Recognition

Zhou Junwei, Hongchao Gao, Jiao Dai, Dongqin Liu, Jizhong Han

Responsive image

Auto-TLDR; Multi-head Self-relation Network for Scene Text Recognition

Slides Poster Similar

The text embedded in scene images can be seen everywhere in our lives. However, recognizing text from natural scene images is still a challenge because of its diverse shapes and distorted patterns. Recently, advanced recognition networks generally treat scene text recognition as a sequence prediction task. Although achieving excellent performance, these recognition networks consider the feature map cells as independent individuals and update cells state without utilizing the information of their neighboring cells. And the local receptive field of traditional convolutional neural network (CNN) makes a single cell that cannot cover the whole text region in an image. Due to these issues, the existing recognition networks cannot extract the global context in a visual scene. To deal with the above problems, we propose a Multi-head Self-relation Network(MSRN) for scene text recognition in this paper. The MSRN consists of several multi-head self-relation layers, which is designed for extracting the global context of a visual scene, so that transforms a cell into a new cell that fuses the information of the related cells. Furthermore, experiments over several public datasets demonstrate that our proposed recognition network achieves superior performance on several benchmark datasets including IC03, IC13, IC15, SVT-Perspective.

Weakly Supervised Attention Rectification for Scene Text Recognition

Chengyu Gu, Shilin Wang, Yiwei Zhu, Zheng Huang, Kai Chen

Responsive image

Auto-TLDR; An auxiliary supervision branch for attention-based scene text recognition

Slides Poster Similar

Scene text recognition has become a hot topic in recent years due to its booming real-life applications. Attention-based encoder-decoder framework has become one of the most popular frameworks especially in the irregular text scenario. However, the “attention drift” problem reduces the recognition performance for most existing attention-based scene text recognition methods. To solve this problem, we propose an auxiliary supervision branch along with the attention-based encoder-decoder framework. A new loss function is designed to refine the feature map and to help the attention region align the target character area. Compared with existing attention rectification mechanisms, our method does not require character-level annotations or introduce any additional trainable parameter. Furthermore, our method can improve the performance for both RNN-Attention and Scaled Dot-Product Attention. The experiment results on various benchmarks have demonstrated that the proposed approach outperforms the state-of-the-art methods in both regular and irregular text recognition scenarios.

ReADS: A Rectified Attentional Double Supervised Network for Scene Text Recognition

Qi Song, Qianyi Jiang, Xiaolin Wei, Nan Li, Rui Zhang

Responsive image

Auto-TLDR; ReADS: Rectified Attentional Double Supervised Network for General Scene Text Recognition

Slides Poster Similar

In recent years, scene text recognition is always regarded as a sequence-to-sequence problem. Connectionist Temporal Classification (CTC) and Attentional sequence recognition (Attn) are two very prevailing approaches to tackle this problem while they may fail in some scenarios respectively. CTC concentrates more on every individual character but is weak in text semantic dependency modeling. Attn based methods have better context semantic modeling ability while tends to overfit on limited training data. In this paper, we elaborately design a Rectified Attentional Double Supervised Network (ReADS) for general scene text recognition. To overcome the weakness of CTC and Attn, both of them are applied in our method but with different modules in two supervised branches which can make a complementary to each other. Moreover, effective spatial and channel attention mechanisms are introduced to eliminate background noise and extract valid foreground information. Finally, a simple rectified network is implemented to rectify irregular text. The ReADS can be trained end-to-end and only word-level annotations are required. Extensive experiments on various benchmarks verify the effectiveness of ReADS which achieves state-of-the-art performance.

Gaussian Constrained Attention Network for Scene Text Recognition

Zhi Qiao, Xugong Qin, Yu Zhou, Fei Yang, Weiping Wang

Responsive image

Auto-TLDR; Gaussian Constrained Attention Network for Scene Text Recognition

Slides Poster Similar

Scene text recognition has been a hot topic in computer vision. Recent methods adopt the attention mechanism for sequence prediction which achieve convincing results. However, we argue that the existing attention mechanism faces the problem of attention diffusion, in which the model may not focus on a certain character area. In this paper, we propose Gaussian Constrained Attention Network to deal with this problem. It is a 2D attention-based method integrated with a novel Gaussian Constrained Refinement Module, which predicts an additional Gaussian mask to refine the attention weights. Different from adopting an additional supervision on the attention weights simply, our proposed method introduce an explicit refinement. In this way, the attention weights will be more concentrated and the attention-based recognition network achieves better performance. The proposed Gaussian Constrained Refinement Module is flexible and can be applied to existing attention-based methods directly. The experiments on several benchmark datasets demonstrate the effectiveness of our proposed method. Our code has been available at https://github.com/Pay20Y/GCAN.

IBN-STR: A Robust Text Recognizer for Irregular Text in Natural Scenes

Xiaoqian Li, Jie Liu, Shuwu Zhang

Responsive image

Auto-TLDR; IBN-STR: A Robust Text Recognition System Based on Data and Feature Representation

Poster Similar

Although text recognition methods based on deep neural networks have promising performance, there are still challenges due to the variety of text styles, perspective distortion, text with large curvature, and so on. To obtain a robust text recognizer, we have improved the performance from two aspects: data aspect and feature representation aspect. In terms of data, we transform the input images into S-shape distorted images in order to increase the diversity of training data. Besides, we explore the effects of different training data. In terms of feature representation, the combination of instance normalization and batch normalization improves the model's capacity and generalization ability. This paper proposes a robust text recognizer IBN-STR, which is an attention-based model. Through extensive experiments, the model analysis and comparison have been carried out from the aspects of data and feature representation, and the effectiveness of IBN-STR on both regular and irregular text instances has been verified. Furthermore, IBN-STR is an end-to-end recognition system that can achieve state-of-the-art performance.

Recognizing Multiple Text Sequences from an Image by Pure End-To-End Learning

Zhenlong Xu, Shuigeng Zhou, Fan Bai, Cheng Zhanzhan, Yi Niu, Shiliang Pu

Responsive image

Auto-TLDR; Pure End-to-End Learning for Multiple Text Sequences Recognition from Images

Slides Poster Similar

We address a challenging problem: recognizing multiple text sequences from an image by pure end-to-end learning. It is twofold: 1) Multiple text sequences recognition. Each image may contain multiple text sequences of different content, location and orientation, we try to recognize all these texts in the image. 2) Pure end-to-end (PEE) learning.We solve the problem in a pure end-to-end learning way where each training image is labeled by only text transcripts of the contained sequences, without any geometric annotations. Most existing works recognize multiple text sequences from an image in a non-end-to-end (NEE) or quasi-end-to-end (QEE) way, in which each image is trained with both text transcripts and text locations. Only recently, a PEE method was proposed to recognize text sequences from an image where the text sequence was split to several lines in the image. However, it cannot be directly applied to recognizing multiple text sequences from an image. So in this paper, we propose a pure end-to-end learning method to recognize multiple text sequences from an image. Our method directly learns the probability distribution of multiple sequences conditioned on each input image, and outputs multiple text transcripts with a well-designed decoding strategy. To evaluate the proposed method, we construct several datasets mainly based on an existing public dataset and two real application scenarios. Experimental results show that the proposed method can effectively recognize multiple text sequences from images, and outperforms CTC-based and attention-based baseline methods.

Text Recognition - Real World Data and Where to Find Them

Klára Janoušková, Lluis Gomez, Dimosthenis Karatzas, Jiri Matas

Responsive image

Auto-TLDR; Exploiting Weakly Annotated Images for Text Extraction

Slides Poster Similar

We present a method for exploiting weakly annotated images to improve text extraction pipelines. The approach uses an arbitrary end-to-end text recognition system to obtain text region proposals and their, possibly erroneous, transcriptions. The proposed method includes matching of imprecise transcription to weak annotations and edit distance guided neighbourhood search. It produces nearly error-free, localised instances of scene text, which we treat as "pseudo ground truth" (PGT). We apply the method to two weakly-annotated datasets. Training with the extracted PGT consistently improves the accuracy of a state of the art recognition model, by 3.7 % on average, across different benchmark datasets (image domains) and 24.5 % on one of the weakly annotated datasets.

2D License Plate Recognition based on Automatic Perspective Rectification

Hui Xu, Zhao-Hong Guo, Da-Han Wang, Xiang-Dong Zhou, Yu Shi

Responsive image

Auto-TLDR; Perspective Rectification Network for License Plate Recognition

Slides Poster Similar

License plate recognition (LPR) remains a challenging task in face of some difficulties such as image deformation and multi-line character distribution. Text rectification that is crucial to eliminate the effects of image deformation has attracted increasing attentions in scene text recognition. However, current text rectification methods are not designed specifically for LPR, which did not take the features of plate deformation into account. Considering the fact that a license plate (LP) can only generate perspective distortion in the image due to its rigid feature, in this paper we propose a novel perspective rectification network (PRN) to automatically estimate the perspective transformation and rectify the distorted LP accordingly. For recognition, we propose a location-aware 2D attention based recognition network that is capable of recognizing both single-line and double-line plates with perspective deformation. The rectification network and recognition network are connected for end-to-end training. Experiments on common datasets show that the proposed method achieves the state-of-the-art performance, demonstrating the effectiveness of the proposed approach.

Text Recognition in Real Scenarios with a Few Labeled Samples

Jinghuang Lin, Cheng Zhanzhan, Fan Bai, Yi Niu, Shiliang Pu, Shuigeng Zhou

Responsive image

Auto-TLDR; Few-shot Adversarial Sequence Domain Adaptation for Scene Text Recognition

Slides Poster Similar

Scene text recognition (STR) is still a hot research topic in computer vision field due to its various applications. Existing works mainly focus on learning a general model with a huge number of synthetic text images to recognize unconstrained scene texts, and have achieved substantial progress. However, these methods are not quite applicable in many real-world scenarios where 1) high recognition accuracy is required, while 2) labeled samples are lacked. To tackle this challenging problem, this paper proposes a few-shot adversarial sequence domain adaptation (FASDA) approach to build sequence adaptation between the synthetic source domain (with many synthetic labeled samples) and a specific target domain (with only some or a few real labeled samples). This is done by simultaneously learning each character’s feature representation with an attention mech- anism and establishing the corresponding character-level latent subspace with adversarial learning. Our approach can maximize the character-level confusion between the source domain and the target domain, thus achieves the sequence-level adaptation with even a small number of labeled samples in the target domain. Extensive experiments on various datasets show that our method significantly outperforms the finetuning scheme, and obtains comparable performance to the state-of-the-art STR methods.

Stratified Multi-Task Learning for Robust Spotting of Scene Texts

Kinjal Dasgupta, Sudip Das, Ujjwal Bhattacharya

Responsive image

Auto-TLDR; Feature Representation Block for Multi-task Learning of Scene Text

Slides Similar

Gaining control over the dynamics of multi-task learning should help to unlock the potential of the deep network to a great extent. In the existing multi-task learning (MTL) approaches of deep network, all the parameters of its feature encoding part are subjected to adjustments corresponding to each of the underlying sub-tasks. On the other hand, different functional areas of human brain are responsible for distinct functions such as the Broca's area of the cerebrum is responsible for speech formation whereas its Wernicke's area is related to the language development etc. Inspired by this fact, in the present study, we propose to introduce a block (termed as Feature Representation Block) of connection weights spanned over a few successive layers of a deep multi-task learning architecture and stratify the same into distinct subsets for their adjustments exclusively corresponding to different sub-tasks. Additionally, we have introduced a novel regularization component for controlled training of this Feature Representation Block. The purpose of the development of this learning framework is efficient end-to-end recognition of scene texts. Simulation results of the proposed strategy on various benchmark scene text datasets such as ICDAR 2015, ICDAR 2017 MLT, COCO-Text and MSRA-TD500 have improved respective SOTA performance.

Cross-Lingual Text Image Recognition Via Multi-Task Sequence to Sequence Learning

Zhuo Chen, Fei Yin, Xu-Yao Zhang, Qing Yang, Cheng-Lin Liu

Responsive image

Auto-TLDR; Cross-Lingual Text Image Recognition with Multi-task Learning

Slides Poster Similar

This paper considers recognizing texts shown in a source language and translating into a target language, without generating the intermediate source language text image recognition results. We call this problem Cross-Lingual Text Image Recognition (CLTIR). To solve this problem, we propose a multi-task system containing a main task of CLTIR and an auxiliary task of Mono-Lingual Text Image Recognition (MLTIR) simultaneously. Two different sequence to sequence learning methods, a convolution based attention model and a BLSTM model with CTC, are adopted for these tasks respectively. We evaluate the system on a newly collected Chinese-English bilingual movie subtitle image dataset. Experimental results demonstrate the multi-task learning framework performs superiorly in both languages.

Cost-Effective Adversarial Attacks against Scene Text Recognition

Mingkun Yang, Haitian Zheng, Xiang Bai, Jiebo Luo

Responsive image

Auto-TLDR; Adversarial Attacks on Scene Text Recognition

Slides Poster Similar

Scene text recognition is a challenging task due to the diversity in text appearance and complexity of natural scenes. Thanks to the development of deep learning and the large volume of training data, scene text recognition has made impressive progress in recent years. However, recent research on adversarial examples has shown that deep learning models are vulnerable to adversarial input with imperceptible changes. As one of the most practical tasks in computer vision, scene text recognition is also facing huge security risks. To our best knowledge, there has been no work on adversarial attacks against scene text recognition. To investigate its effects on scene text recognition, we make the first attempt to attack the state-of-the-art scene text recognizer, i.e., attention-based recognizer. To that end, we first adjust the objective function designed for non-sequential tasks, such as image classification, semantic segmentation and image retrieval, to the sequential form. We then propose a novel and effective objective function to further reduce the amount of perturbation while achieving a higher attack success rate. Comprehensive experiments on several standard benchmarks clearly demonstrate effective adversarial effects on scene text recognition by the proposed attacks.

Global Context-Based Network with Transformer for Image2latex

Nuo Pang, Chun Yang, Xiaobin Zhu, Jixuan Li, Xu-Cheng Yin

Responsive image

Auto-TLDR; Image2latex with Global Context block and Transformer

Slides Poster Similar

Image2latex usually means converts mathematical formulas in images into latex markup. It is a very challenging job due to the complex two-dimensional structure, variant scales of input, and very long representation sequence. Many researchers use encoder-decoder based model to solve this task and achieved good results. However, these methods don't make full use of the structure and position information of the formula. %In this paper, we improve the encoder by employing Global Context block and Transformer. To solve this problem, we propose a global context-based network with transformer that can (1) learn a more powerful and robust intermediate representation via aggregating global features and (2) encode position information explicitly and (3) learn latent dependencies between symbols by using self-attention mechanism. The experimental results on the dataset IM2LATEX-100K demonstrate the effectiveness of our method.

Sample-Aware Data Augmentor for Scene Text Recognition

Guanghao Meng, Tao Dai, Shudeng Wu, Bin Chen, Jian Lu, Yong Jiang, Shutao Xia

Responsive image

Auto-TLDR; Sample-Aware Data Augmentation for Scene Text Recognition

Slides Poster Similar

Deep neural networks (DNNs) have been widely used in scene text recognition, and achieved remarkable performance. Such DNN-based scene text recognizers usually require plenty of training data for training, but data collection and annotation is usually cost-expensive in practice. To alleviate this issue, data augmentation is often applied to train the scene text recognizers. However, existing data augmentation methods including affine transformation and elastic transformation methods suffer from the problems of under- and over-diversity, due to the complexity of text contents and shapes. In this paper, we propose a sample-aware data augmentor to transform samples adaptively based on the contents of samples. Specifically, our data augmentor consists of three parts: gated module, affine transformation module, and elastic transformation module. In our data augmentor, affine transformation module focuses on keeping the affinity of samples, while elastic transformation module aims to improve the diversity of samples. With the gated module, our data augmentor determines transformation type adaptively based on the properties of training samples and the recognizer capability during the training process. Besides, our framework introduces an adversarial learning strategy to optimize the augmentor and the recognizer jointly. Extensive experiments on scene text recognition benchmarks show that our sample-aware data augmentor significantly improves the performance of state-of-the-art scene text recognizer.

Robust Lexicon-Free Confidence Prediction for Text Recognition

Qi Song, Qianyi Jiang, Rui Zhang, Xiaolin Wei

Responsive image

Auto-TLDR; Confidence Measurement for Optical Character Recognition using Single-Input Multi-Output Network

Slides Poster Similar

Benefiting from the success of deep learning, Optical Character Recognition (OCR) is booming in recent years. As we all know, the text recognition results are vulnerable to slight perturbation in input images, thus a method for measuring how reliable the results are is crucial. In this paper, we present a novel method for confidence measurement given a text recognition result, which can be embedded in any text recognizer with little overheads. Our method consists of two stages with a coarse-to-fine style. The first stage generates multiple candidates for voting coarse scores by a Single-Input Multi-Output network (SIMO). The second stage calculates a refined confidence score referred by the voting result and the conditional probabilities of the Top-1 probable recognition sequence. Highly competitive performance is achieved on several standard benchmarks validates the efficiency and effectiveness of the proposed method. Moreover, it can be adopted in both Latin and non-Latin languages.

A Transformer-Based Radical Analysis Network for Chinese Character Recognition

Chen Yang, Qing Wang, Jun Du, Jianshu Zhang, Changjie Wu, Jiaming Wang

Responsive image

Auto-TLDR; Transformer-based Radical Analysis Network for Chinese Character Recognition

Slides Poster Similar

Recently, a novel radical analysis network (RAN) has the capability of effectively recognizing unseen Chinese character classes and largely reducing the requirement of training data by treating a Chinese character as a hierarchical composition of radicals rather than a single character class.} However, when dealing with more challenging issues, such as the recognition of complicated characters, low-frequency character categories, and characters in natural scenes, RAN still has a lot of room for improvement. In this paper, we explore options to further improve the structure generalization and robustness capability of RAN with the Transformer architecture, which has achieved start-of-the-art results for many sequence-to-sequence tasks. More specifically, we propose to replace the original attention module in RAN with the transformer decoder, which is named as a transformer-based radical analysis network (RTN). The experimental results show that the proposed approach can significantly outperform the RAN on both printed Chinese character database and natural scene Chinese character database. Meanwhile, further analysis proves that RTN can be better generalized to complex samples and low-frequency characters, and has better robustness in recognizing Chinese characters with different attributes.

Feature Embedding Based Text Instance Grouping for Largely Spaced and Occluded Text Detection

Pan Gao, Qi Wan, Renwu Gao, Linlin Shen

Responsive image

Auto-TLDR; Text Instance Embedding Based Feature Embeddings for Multiple Text Instance Grouping

Slides Poster Similar

A text instance can be easily detected as multiple ones due to the large space between texts/characters, curved shape and partial occlusion. In this paper, a feature embedding based text instance grouping algorithm is proposed to solve this problem. To learn the feature space, a TIEM (Text Instance Embedding Module) is trained to minimize the within instance scatter and maximize the between instance scatter. Similarity between different text instances are measured in the feature space and merged if they meet certain conditions. Experimental results show that our approach can effectively connect text regions that belong to the same text instance. Competitive performance of our approach has been achieved on CTW1500, Total-Text, IC15 and a subset consists of texts selected from the three datasets, with large spacing and occlusions.

Dual Path Multi-Modal High-Order Features for Textual Content Based Visual Question Answering

Yanan Li, Yuetan Lin, Hongrui Zhao, Donghui Wang

Responsive image

Auto-TLDR; TextVQA: An End-to-End Visual Question Answering Model for Text-Based VQA

Slides Similar

As a typical cross-modal problem, visual question answering (VQA) has received increasing attention from the communities of computer vision and natural language processing. Reading and reasoning about texts and visual contents in the images is a burgeoning and important research topic in VQA, especially for the visually impaired assistance applications. Given an image, it aims to predict an answer to a provided natural language question closely related to its textual contents. In this paper, we propose a novel end-to-end textual content based VQA model, which grounds question answering both on the visual and textual information. After encoding the image, question and recognized text words, it uses multi-modal factorized high-order modules and the attention mechanism to fuse question-image and question-text features respectively. The complex correlations among different features can be captured efficiently. To ensure the model's extendibility, it embeds candidate answers and recognized texts in a semantic embedding space and adopts semantic embedding based classifier to perform answer prediction. Extensive experiments on the newly proposed benchmark TextVQA demonstrate that the proposed model can achieve promising results.

DUET: Detection Utilizing Enhancement for Text in Scanned or Captured Documents

Eun-Soo Jung, Hyeonggwan Son, Kyusam Oh, Yongkeun Yun, Soonhwan Kwon, Min Soo Kim

Responsive image

Auto-TLDR; Text Detection for Document Images Using Synthetic and Real Data

Slides Poster Similar

We present a novel approach to text detection for document images. For robust text detection of noisy scanned or captured document images, the advantages of multi-task learning are adopted by adding an auxiliary task of text enhancement. Consequently, our proposed model trains reducing noise and enhancing text regions as well as detecting text. To overcome the insufficiency of document image data for text detection, train data for our model are enriched with synthesized document images that are fully labeled for text detection and enhancement. For the effective use of synthetic and real data, the proposed model is trained in two phases. The first phase is training only synthetic data in a fully-supervised manner. Then real data with only detection labels are added in the second phase. The enhancement task for real data is weakly-supervised with information from detection labels. Our methods are demonstrated on a real document dataset with performances exceeding those of other methods. Also, we conducted ablations to analyze effects of the synthetic data, multi-task, and weak-supervision. Whereas the existing text detection studies mostly focus on the text in scenes, our proposed method is optimized to the applications for the text in scanned or captured documents.

Local Gradient Difference Based Mass Features for Classification of 2D-3D Natural Scene Text Images

Lokesh Nandanwar, Shivakumara Palaiahnakote, Raghavendra Ramachandra, Tong Lu, Umapada Pal, Daniel Lopresti, Nor Badrul Anuar

Responsive image

Auto-TLDR; Classification of 2D and 3D Natural Scene Images Using COLD

Slides Poster Similar

Methods developed for normal 2D text detection do not work well for a text that is rendered using decorative, 3D effects. This paper proposes a new method for classification of 2D and 3D natural scene images such that an appropriate method can be chosen or modified according to the complexity of the individual classes. The proposed method explores local gradient differences for obtaining candidate pixels, which represent a stroke. To study the spatial distribution of candidate pixels, we propose a measure we call COLD, which is denser for pixels toward the center of strokes and scattered for non-stroke pixels. This observation leads us to introduce mass features for extracting the regular spatial pattern of COLD, which indicates a 2D text image. The extracted features are fed to a Neural Network (NN) for classification. The proposed method is tested on both a new dataset introduced in this work and a standard dataset assembled from different natural scene datasets, and compared to from existing methods to show its effectiveness. The approach improves text detection performance significantly after classification.

Self-Training for Domain Adaptive Scene Text Detection

Yudi Chen, Wei Wang, Yu Zhou, Fei Yang, Dongbao Yang, Weiping Wang

Responsive image

Auto-TLDR; A self-training framework for image-based scene text detection

Slides Similar

Though deep learning based scene text detection has achieved great progress, well-trained detectors suffer from severe performance degradation for different domains. In general, a tremendous amount of data is indispensable to train the detector in the target domain. However, data collection and annotation are expensive and time-consuming. To address this problem, we propose a self-training framework to automatically mine hard examples with pseudo-labels from unannotated videos or images. To reduce the noise of hard examples, a novel text mining module is implemented based on the fusion of detection and tracking results. Then, an image-to-video generation method is designed for the tasks that videos are unavailable and only images can be used. Experimental results on standard benchmarks, including ICDAR2015, MSRA-TD500, ICDAR2017 MLT, demonstrate the effectiveness of our self-training method. The simple Mask R-CNN adapted with self-training and fine-tuned on real data can achieve comparable or even superior results with the state-of-the-art methods.

Scene Text Detection with Selected Anchors

Anna Zhu, Hang Du, Shengwu Xiong

Responsive image

Auto-TLDR; AS-RPN: Anchor Selection-based Region Proposal Network for Scene Text Detection

Slides Poster Similar

Object proposal technique with dense anchoring scheme for scene text detection were applied frequently to achieve high recall. It results in the significant improvement in accuracy but waste of computational searching, regression and classification. In this paper, we propose an anchor selection-based region proposal network (AS-RPN) using effective selected anchors instead of dense anchors to extract text proposals. The center, scales, aspect ratios and orientations of anchors are learnable instead of fixing, which leads to high recall and greatly reduced numbers of anchors. By replacing the anchor-based RPN in Faster RCNN, the AS-RPN-based Faster RCNN can achieve comparable performance with previous state-of-the-art text detecting approaches on standard benchmarks, including COCO-Text, ICDAR2013, ICDAR2015 and MSRA-TD500 when using single-scale and single model (ResNet50) testing only.

Watch Your Strokes: Improving Handwritten Text Recognition with Deformable Convolutions

Iulian Cojocaru, Silvia Cascianelli, Lorenzo Baraldi, Massimiliano Corsini, Rita Cucchiara

Responsive image

Auto-TLDR; Deformable Convolutional Neural Networks for Handwritten Text Recognition

Slides Poster Similar

Handwritten Text Recognition (HTR) in free-layout pages is a valuable yet challenging task which aims to automatically understand handwritten texts. State-of-the-art approaches in this field usually encode input images with Convolutional Neural Networks, whose kernels are typically defined on a fixed grid and focus on all input pixels independently. However, this is in contrast with the sparse nature of handwritten pages, in which only pixels representing the ink of the writing are useful for the recognition task. Furthermore, the standard convolution operator is not explicitly designed to take into account the great variability in shape, scale, and orientation of handwritten characters. To overcome these limitations, we investigate the use of deformable convolutions for handwriting recognition. This type of convolution deform the convolution kernel according to the content of the neighborhood, and can therefore be more adaptable to geometric variations and other deformations of the text. Experiments conducted on the IAM and RIMES datasets demonstrate that the use of deformable convolutions is a promising direction for the design of novel architectures for handwritten text recognition.

TCATD: Text Contour Attention for Scene Text Detection

Ziling Hu, Wu Xingjiao, Jing Yang

Responsive image

Auto-TLDR; Text Contour Attention Text Detector

Slides Poster Similar

Segmentation-based approaches have enabled state-of-the-art performance in long or curved text detection tasks. However, false detection still is a challenge when two text instances are close to each other. To address this problem, in this paper, we propose a Text Contour Attention Text Detector (TCATD), which can locate scene text with arbitrary orientation and shape accurately. Different from previous work, TCATD focus on text contour map (TC), text center intensity map (TCI) and text kernel maps (TK). The TC can introduce text contour information, the TCI can help to learn the accurate text segmentation and the TK can generate the complete shape of text instances. Besides, we propose a Text Contour Attention Module to deal with contour information. After the Text Contour Attention Module, TC, TCI and TK will be obtained. Extensive experiments on ICDAR2015, CTW1500 and Total-Text demonstrate that the proposed method achieves the state-of-the-art performance.

An Accurate Threshold Insensitive Kernel Detector for Arbitrary Shaped Text

Xijun Qian, Yifan Liu, Yu-Bin Yang

Responsive image

Auto-TLDR; TIKD: threshold insensitive kernel detector for arbitrary shaped text

Slides Similar

Recently, segmentation-based methods are popular in scene text detection due to the segmentation results can easily represent scene text of arbitrary shapes. However, previous works segment text instances the same as normal objects. It is obvious that the edge of the text instance differs from normal objects. In this paper, we propose a threshold insensitive kernel detector for arbitrary shaped text called TIKD, which includes a simple but stable base model and a new loss weight called Decay Loss Weight (DLW). By suppressing outlier pixels in a gradual way, the DLW can lead the network to detect more accurate text instances. Our method shows great power in accuracy and stability. It is worth mentioning that we achieve the precision, recall, f-measure of 88.7%, 83.7%, 86.1% respectively on the Total-Text dataset, with a fast speed of 16.3 frames per second. What’s more, even if we set the threshold in an extreme situation range from 0.1 to 0.9, our method can always achieve a stable f-measure over 79.9% on the Total-Text dataset.

Mutually Guided Dual-Task Network for Scene Text Detection

Mengbiao Zhao, Wei Feng, Fei Yin, Xu-Yao Zhang, Cheng-Lin Liu

Responsive image

Auto-TLDR; A dual-task network for word-level and line-level text detection

Slides Similar

Scene text detection has been studied extensively. Existing methods detect either words or text lines and use either word-level or line-level annotated data for training. In this paper, we propose a dual-task network that can perform word-level and line-level text detection simultaneously and use training data of both levels of annotation to boost the performance. The dual-task network has two detection heads for word-level and line-level text detection, respectively. Then we propose a mutual guidance scheme for the joint training of the two tasks with two modules: line filtering module utilizes the output of the text line detector to filter out the non-text regions for the word detector, and word enhancing module provides prior positions of words for the text line detector depending on the output of the word detector. Experimental results of word-level and line-level text detection demonstrate the effectiveness of the proposed dual-task network and mutual guidance scheme, and the results of our method are competitive with state-of-the-art methods.

LODENet: A Holistic Approach to Offline Handwritten Chinese and Japanese Text Line Recognition

Huu Tin Hoang, Chun-Jen Peng, Hung Tran, Hung Le, Huy Hoang Nguyen

Responsive image

Auto-TLDR; Logographic DEComposition Encoding for Chinese and Japanese Text Line Recognition

Slides Poster Similar

One of the biggest obstacles in Chinese and Japanese text line recognition is how to present their enormous character sets. The most common solution is to merely choose and represent a small subset of characters using one-hot encoding. However, such an approach is costly to describe huge character sets, and ignores their semantic relationships. Recent studies have attempted to utilize different encoding methods, but they struggle to build a bijection mapping. In this work, we propose a novel encoding method, called LOgographic DEComposition encoding (LODEC), that can efficiently perform a 1-to-1 mapping for all Chinese and Japanese characters with a strong awareness of semantic relationships. As such, LODEC enables to encode over 21,000 Chinese and Japanese characters by only 520 fundamental elements. Moreover, to handle the vast variety of handwritten texts in the two languages, we propose a novel deep learning (DL) architecture, called LODENet, together with an end-to-end training scheme, that leverages auxiliary data generated by LODEC or other radical-based encoding methods. We performed systematic experiments on both Chinese and Japanese datasets, and found that our approach surpassed the performance of state-of-the-art baselines. Furthermore, empirical evidence shows that our method can gain significantly better results using synthesized text line images without the need for domain knowledge.

PICK: Processing Key Information Extraction from Documents Using Improved Graph Learning-Convolutional Networks

Wenwen Yu, Ning Lu, Xianbiao Qi, Ping Gong, Rong Xiao

Responsive image

Auto-TLDR; PICK: A Graph Learning Framework for Key Information Extraction from Documents

Slides Poster Similar

Computer vision with state-of-the-art deep learning models have achieved huge success in the field of Optical Character Recognition (OCR) including text detection and recognition tasks recently. However, Key Information Extraction (KIE) from documents as the downstream task of OCR, having a large number of use scenarios in real-world, remains a challenge because documents not only have textual features extracting from OCR systems but also have semantic visual features that are not fully exploited and play a critical role in KIE. Too little work has been devoted to efficiently make full use of both textual and visual features of the documents. In this paper, we introduce PICK, a framework that is effective and robust in handling complex documents layout for KIE by combining graph learning with graph convolution operation, yielding a richer semantic representation containing the textual and visual features and global layout without ambiguity. Extensive experiments on real-world datasets have been conducted to show that our method outperforms baselines methods by significant margins.

Multi-Task Learning Based Traditional Mongolian Words Recognition

Hongxi Wei, Hui Zhang, Jing Zhang, Kexin Liu

Responsive image

Auto-TLDR; Multi-task Learning for Mongolian Words Recognition

Slides Poster Similar

In this paper, a multi-task learning framework has been proposed for solving and improving traditional Mongolian words recognition. To be specific, a sequence-to-sequence model with attention mechanism was utilized to accomplish the task of recognition. Therein, the attention mechanism is designed to fulfill the task of glyph segmentation during the process of recognition. Although the glyph segmentation is an implicit operation, the information of glyph segmentation can be integrated into the process of recognition. After that, the two tasks can be accomplished simultaneously under the framework of multi-task learning. By this way, adjacent image frames can be decoded into a glyph more precisely, which results in improving not only the performance of words recognition but also the accuracy of character segmentation. Experimental results demonstrate that the proposed multi-task learning based scheme outperforms the conventional glyph segmentation-based method and various segmentation-free (i.e. holistic recognition) methods.

Multi-Modal Contextual Graph Neural Network for Text Visual Question Answering

Yaoyuan Liang, Xin Wang, Xuguang Duan, Wenwu Zhu

Responsive image

Auto-TLDR; Multi-modal Contextual Graph Neural Network for Text Visual Question Answering

Slides Poster Similar

Text visual question answering (TextVQA) targets at answering the question related to texts appearing in the given images, posing more challenges than VQA by requiring a deeper recognition and understanding of various shapes of human-readable scene texts as well as their meanings in different contexts. Existing works on TextVQA suffer from two weaknesses: i) scene texts and non-textual objects are processed separately and independently without considering their mutual interactions during the question understanding and answering process, ii) scene texts are encoded only through word embeddings without taking the corresponding visual appearance features as well as their potential relationships with other non-textual objects in the images into account. To overcome the weakness of exiting works, we propose a novel multi-modal contextual graph neural network (MCG) model for TextVQA. The proposed MCG model can capture the relationships between visual features of scene texts and non-textual objects in the given images as well as utilize richer sources of multi-modal features to improve the model performance. In particular, we encode the scene texts into richer features containing textual, visual and positional features, then model the visual relations between scene texts and non-textual objects through a contextual graph neural network. Our extensive experiments on real-world dataset demonstrate the advantages of the proposed MCG model over baseline approaches.

ConvMath : A Convolutional Sequence Network for Mathematical Expression Recognition

Zuoyu Yan, Xiaode Zhang, Liangcai Gao, Ke Yuan, Zhi Tang

Responsive image

Auto-TLDR; Convolutional Sequence Modeling for Mathematical Expressions Recognition

Slides Poster Similar

Despite the recent advances in optical character recognition (OCR), mathematical expressions still face a great challenge to recognize due to their two-dimensional graphical layout. In this paper, we propose a convolutional sequence modeling network, ConvMath, which converts the mathematical expression description in an image into a LaTeX sequence in an end-to-end way. The network combines an image encoder for feature extraction and a convolutional decoder for sequence generation. Compared with other Long Short Term Memory(LSTM) based encoder-decoder models, ConvMath is entirely based on convolution, thus it is easy to perform parallel computation. Besides, the network adopts multi-layer attention mechanism in the decoder, which allows the model to align output symbols with source feature vectors automatically, and alleviates the problem of lacking coverage while training the model. The performance of ConvMath is evaluated on an open dataset named IM2LATEX-100K, including 103556 samples. The experimental results demonstrate that the proposed network achieves state-of-the-art accuracy and much better efficiency than previous methods.

Transferable Adversarial Attacks for Deep Scene Text Detection

Shudeng Wu, Tao Dai, Guanghao Meng, Bin Chen, Jian Lu, Shutao Xia

Responsive image

Auto-TLDR; Robustness of DNN-based STD methods against Adversarial Attacks

Slides Similar

Scene text detection (STD) aims to locate text in images and plays an important role in many computer vision tasks including automatic driving and text recognition systems. Recently, deep neural networks (DNNs) have been widely and successfully used in scene text detection, leading to plenty of DNN-based STD methods including regression-based and segmentation-based STD methods. However, recent studies have also shown that DNN is vulnerable to adversarial attacks, which can significantly degrade the performance of DNN models. In this paper, we investigate the robustness of DNN-based STD methods against adversarial attacks. To this end, we propose a generic and efficient attack method to generate adversarial examples, which are produced by adding small but imperceptible adversarial perturbation to the input images. Experiments on attacking four various models and a real-world STD engine of Google optical character recognition (OCR) show that the state-of-the-art DNN-based STD methods including regression-based and segmentation-based methods are vulnerable to adversarial attacks.

Edge-Aware Graph Attention Network for Ratio of Edge-User Estimation in Mobile Networks

Jiehui Deng, Sheng Wan, Xiang Wang, Enmei Tu, Xiaolin Huang, Jie Yang, Chen Gong

Responsive image

Auto-TLDR; EAGAT: Edge-Aware Graph Attention Network for Automatic REU Estimation in Mobile Networks

Slides Poster Similar

Estimating the Ratio of Edge-Users (REU) is an important issue in mobile networks, as it helps the subsequent adjustment of loads in different cells. However, existing approaches usually determine the REU manually, which are experience-dependent and labor-intensive, and thus the estimated REU might be imprecise. Considering the inherited graph structure of mobile networks, in this paper, we utilize a graph-based deep learning method for automatic REU estimation, where the practical cells are deemed as nodes and the load switchings among them constitute edges. Concretely, Graph Attention Network (GAT) is employed as the backbone of our method due to its impressive generalizability in dealing with networked data. Nevertheless, conventional GAT cannot make full use of the information in mobile networks, since it only incorporates node features to infer the pairwise importance and conduct graph convolutions, while the edge features that are actually critical in our problem are disregarded. To accommodate this issue, we propose an Edge-Aware Graph Attention Network (EAGAT), which is able to fuse the node features and edge features for REU estimation. Extensive experimental results on two real-world mobile network datasets demonstrate the superiority of our EAGAT approach to several state-of-the-art methods.

Online Trajectory Recovery from Offline Handwritten Japanese Kanji Characters of Multiple Strokes

Hung Tuan Nguyen, Tsubasa Nakamura, Cuong Tuan Nguyen, Masaki Nakagawa

Responsive image

Auto-TLDR; Recovering Dynamic Online Trajectories from Offline Japanese Kanji Character Images for Handwritten Character Recognition

Slides Poster Similar

We propose a deep neural network-based method to recover dynamic online trajectories from offline handwritten Japanese kanji character images. It is a challenging task since Japanese kanji characters consist of multiple strokes. Our proposed model has three main components: Convolutional Neural Network-based encoder, Long Short-Term Memory Network-based decoder with an attention layer, and Gaussian Mixture Model (GMM). The encoder focuses on feature extraction while the decoder refers to the extracted features and generates time-sequences of GMM parameters. The attention layer is the key component for trajectory recovery. The GMM provides robustness to style variations so that the proposed model does not overfit to training samples. In the experiments, the proposed method is evaluated by both visual verification and handwritten character recognition. This is the first attempt to use online recovered trajectories to help improve the performance of offline handwriting recognition. Although the visual verification reveals some problems, the recognition experiments demonstrate the effect of trajectory recovery in improving the accuracy of offline handwritten character recognition when online recognition of the recovered trajectories are combined.

Moto: Enhancing Embedding with Multiple Joint Factors for Chinese Text Classification

Xunzhu Tang, Rujie Zhu, Tiezhu Sun

Responsive image

Auto-TLDR; Moto: Enhancing Embedding with Multiple J\textbf{o}int Fac\textBF{to}rs

Slides Poster Similar

Recently, language representation techniques have achieved great performances in text classification. However, most existing representation models are specifically designed for English materials, which may fail in Chinese because of the huge difference between these two languages. Actually, few existing methods for Chinese text classification process texts at a single level. However, as a special kind of hieroglyphics, radicals of Chinese characters are good semantic carriers. In addition, Pinyin codes carry the semantic of tones, and Wubi reflects the stroke structure information, \textit{etc}. Unfortunately, previous researches neglected to find an effective way to distill the useful parts of these four factors and to fuse them. In our works, we propose a novel model called Moto: Enhancing Embedding with \textbf{M}ultiple J\textbf{o}int Fac\textbf{to}rs. Specifically, we design an attention mechanism to distill the useful parts by fusing the four-level information above more effectively. We conduct extensive experiments on four popular tasks. The empirical results show that our Moto achieves SOTA 0.8316 ($F_1$-score, 2.11\% improvement) on Chinese news titles, 96.38 (1.24\% improvement) on Fudan Corpus and 0.9633 (3.26\% improvement) on THUCNews.

Boosting High-Level Vision with Joint Compression Artifacts Reduction and Super-Resolution

Xiaoyu Xiang, Qian Lin, Jan Allebach

Responsive image

Auto-TLDR; A Context-Aware Joint CAR and SR Neural Network for High-Resolution Text Recognition and Face Detection

Slides Poster Similar

Due to the limits of bandwidth and storage space, digital images are usually down-scaled and compressed when transmitted over networks, resulting in loss of details and jarring artifacts that can lower the performance of high-level visual tasks. In this paper, we aim to generate an artifact-free high-resolution image from a low-resolution one compressed with an arbitrary quality factor by exploring joint compression artifacts reduction (CAR) and super-resolution (SR) tasks. First, we propose a context-aware joint CAR and SR neural network (CAJNN) that integrates both local and non-local features to solve CAR and SR in one-stage. Finally, a deep reconstruction network is adopted to predict high quality and high-resolution images. Evaluation on CAR and SR benchmark datasets shows that our CAJNN model outperforms previous methods and also takes 26.2% less runtime. Based on this model, we explore addressing two critical challenges in high-level computer vision: optical character recognition of low-resolution texts, and extremely tiny face detection. We demonstrate that CAJNN can serve as an effective image preprocessing method and improve the accuracy for real-scene text recognition (from 85.30% to 85.75%) and the average precision for tiny face detection (from 0.317 to 0.611).

Context Matters: Self-Attention for Sign Language Recognition

Fares Ben Slimane, Mohamed Bouguessa

Responsive image

Auto-TLDR; Attentional Network for Continuous Sign Language Recognition

Slides Poster Similar

This paper proposes an attentional network for the task of Continuous Sign Language Recognition. The proposed approach exploits co-independent streams of data to model the sign language modalities. These different channels of information can share a complex temporal structure between each other. For that reason, we apply attention to synchronize and help capture entangled dependencies between the different sign language components. Even though Sign Language is multi-channel, handshapes represent the central entities in sign interpretation. Seeing handshapes in their correct context defines the meaning of a sign. Taking that into account, we utilize the attention mechanism to efficiently aggregate the hand features with their appropriate Spatio-temporal context for better sign recognition. We found that by doing so the model is able to identify the essential Sign Language components that revolve around the dominant hand and the face areas. We test our model on the benchmark dataset RWTH-PHOENIX-Weather 2014, yielding competitive results.

Stroke Based Posterior Attention for Online Handwritten Mathematical Expression Recognition

Changjie Wu, Qing Wang, Jianshu Zhang, Jun Du, Jiaming Wang, Jiajia Wu, Jin-Shui Hu

Responsive image

Auto-TLDR; Posterior Attention for Online Handwritten Mathematical Expression Recognition

Slides Poster Similar

Recently, many researches propose to employ attention based encoder-decoder models to convert a sequence of trajectory points into a LaTeX string for online handwritten mathematical expression recognition (OHMER), and the recognition performance of these models critically relies on the accuracy of the attention. In this paper, unlike previous methods which basically employ a soft attention model, we propose to employ a posterior attention model, which modifies the attention probabilities after observing the output probabilities generated by the soft attention model. In order to further improve the posterior attention mechanism, we propose a stroke average pooling layer to aggregate point-level features obtained from the encoder into stroke-level features. We argue that posterior attention is better to be implemented on stroke-level features than point-level features as the output probabilities generated by stroke is more convincing than generated by point, and we prove that through experimental analysis. Validated on the CROHME competition task, we demonstrate that stroke based posterior attention achieves expression recognition rates of 54.26% on CROHME 2014 and 51.75% on CROHME 2016. According to attention visualization analysis, we empirically demonstrate that the posterior attention mechanism can achieve better alignment accuracy than the soft attention mechanism.

MANet: Multimodal Attention Network Based Point-View Fusion for 3D Shape Recognition

Yaxin Zhao, Jichao Jiao, Ning Li

Responsive image

Auto-TLDR; Fusion Network for 3D Shape Recognition based on Multimodal Attention Mechanism

Slides Poster Similar

3D shape recognition has attracted more and more attention as a task of 3D vision research. The proliferation of 3D data encourages various deep learning methods based on 3D data. Now there have been many deep learning models based on point-cloud data or multi-view data alone. However, in the era of big data, integrating data of two different modals to obtain a unified 3D shape descriptor is bound to improve the recognition accuracy. Therefore, this paper proposes a fusion network based on multimodal attention mechanism for 3D shape recognition. Considering the limitations of multi-view data, we introduce a soft attention scheme, which can use the global point-cloud features to filter the multi-view features, and then realize the effective fusion of the two features. More specifically, we obtain the enhanced multi-view features by mining the contribution of each multi-view image to the overall shape recognition, and then fuse the point-cloud features and the enhanced multi-view features to obtain a more discriminative 3D shape descriptor. We have performed relevant experiments on the ModelNet40 dataset, and experimental results verify the effectiveness of our method.

Attention Pyramid Module for Scene Recognition

Zhinan Qiao, Xiaohui Yuan, Chengyuan Zhuang, Abolfazl Meyarian

Responsive image

Auto-TLDR; Attention Pyramid Module for Multi-Scale Scene Recognition

Slides Poster Similar

The unrestricted open vocabulary and diverse substances of scenery images bring significant challenges to scene recognition. However, most deep learning architectures and attention methods are developed on general-purpose datasets and omit the characteristics of scene data. In this paper, we exploit the attention pyramid module (APM) to tackle the predicament of scene recognition. Our method streamlines the multi-scale scene recognition pipeline, learns comprehensive scene features at various scales and locations, addresses the interdependency among scales, and further assists feature re-calibration as well as aggregation process. APM is extremely light-weighted and can be easily plugged into existing network architectures in a parameter-efficient manner. By simply integrating APM into ResNet-50, we obtain a 3.54\% boost in terms of top-1 accuracy on the benchmark scene dataset. Comprehensive experiments show that APM achieves better performance comparing with state-of-the-art attention methods using significant less computation budget. Code and pre-trained models will be made publicly available.

Improving Word Recognition Using Multiple Hypotheses and Deep Embeddings

Siddhant Bansal, Praveen Krishnan, C. V. Jawahar

Responsive image

Auto-TLDR; EmbedNet: fuse recognition-based and recognition-free approaches for word recognition using learning-based methods

Slides Poster Similar

We propose to fuse recognition-based and recognition-free approaches for word recognition using learning-based methods. For this purpose, results obtained using a text recognizer and deep embeddings (generated using an End2End network) are fused. To further improve the embeddings, we propose EmbedNet, it uses triplet loss for training and learns an embedding space where the embedding of the word image lies closer to its corresponding text transcription’s embedding. This updated embedding space helps in choosing the correct prediction with higher confidence. To further improve the accuracy, we propose a plug-and-play module called Confidence based Accuracy Booster (CAB). It takes in the confidence scores obtained from the text recognizer and Euclidean distances between the embeddings and generates an updated distance vector. This vector has lower distance values for the correct words and higher distance values for the incorrect words. We rigorously evaluate our proposed method systematically on a collection of books that are in the Hindi language. Our method achieves an absolute improvement of around 10% in terms of word recognition accuracy.

VTT: Long-Term Visual Tracking with Transformers

Tianling Bian, Yang Hua, Tao Song, Zhengui Xue, Ruhui Ma, Neil Robertson, Haibing Guan

Responsive image

Auto-TLDR; Visual Tracking Transformer with transformers for long-term visual tracking

Slides Similar

Long-term visual tracking is a challenging problem. State-of-the-art long-term trackers, e.g., GlobalTrack, utilize region proposal networks (RPNs) to generate target proposals. However, the performance of the trackers is affected by occlusions and large scale or ratio variations. To address these issues, in this paper, we are the first to propose a novel architecture with transformers for long-term visual tracking. Specifically, the proposed Visual Tracking Transformer (VTT) utilizes a transformer encoder-decoder architecture for aggregating global information to deal with occlusion and large scale or ratio variation. Furthermore, it also shows better discriminative power against instance-level distractors without the need for extra labeling and hard-sample mining. We conduct extensive experiments on three largest long-term tracking dataset and have achieved state-of-the-art performance.

Recursive Recognition of Offline Handwritten Mathematical Expressions

Marco Cotogni, Claudio Cusano, Antonino Nocera

Responsive image

Auto-TLDR; Online Handwritten Mathematical Expression Recognition with Recurrent Neural Network

Slides Poster Similar

In this paper we propose a method for Offline Handwritten Mathematical Expression recognition. The method is a fast and accurate thanks to its architecture, which include both a Convolutional Neural Network and a Recurrent Neural Network. The CNN extracts features from the image to recognize and its output is provided to the RNN which produces the mathematical expression encoded in the LaTeX language. To process both sequential and non-sequential mathematical expressions we also included a deconvolutional module which, in a recursive way, segments the image for additional analysis trough a recursive process. The results obtained show a very high accuracy obtained on a large handwritten data set of 9100 samples of handwritten expressions.

Label Incorporated Graph Neural Networks for Text Classification

Yuan Xin, Linli Xu, Junliang Guo, Jiquan Li, Xin Sheng, Yuanyuan Zhou

Responsive image

Auto-TLDR; Graph Neural Networks for Semi-supervised Text Classification

Slides Poster Similar

Graph Neural Networks (GNNs) have achieved great success on graph-structured data, and their applications on traditional data structures such as natural language processing and semi-supervised text classification have been extensively explored in recent years. While previous works only consider the text information while building the graph, heterogeneous information such as labels is ignored. In this paper, we consider to incorporate the label information while building the graph by adding text-label-text paths, through which the supervision information will propagate among the graph more directly. Specifically, we treat labels as nodes in the graph which also contains text and word nodes, and then connect labels with texts belonging to that label. Through graph convolutions, label embeddings are jointly learned with text embeddings in the same latent semantic space. The newly incorporated label nodes will facilitate learning more accurate text embeddings by introducing the label information, and thus benefit the downstream text classification tasks. Extensive results on several benchmark datasets show that the proposed framework outperforms baseline methods by a significant margin.

GCNs-Based Context-Aware Short Text Similarity Model

Xiaoqi Sun

Responsive image

Auto-TLDR; Context-Aware Graph Convolutional Network for Text Similarity

Slides Poster Similar

Semantic textual similarity is a fundamental task in text mining and natural language processing (NLP), which has profound research value. The essential step for text similarity is text representation learning. Recently, researches have explored the graph convolutional network (GCN) techniques on text representation, since GCN does well in handling complex structures and preserving syntactic information. However, current GCN models are usually limited to very shallow layers due to the vanishing gradient problem, which cannot capture non-local dependency information of sentences. In this paper, we propose a GCNs-based context-aware (GCSTS) model that applies iterated GCN blocks to train deeper GCNs. Recurrently employing the same GCN block prevents over-fitting and provides broad effective input width. Combined with dense connections, GCSTS can be trained more deeply. Besides, we use dynamic graph structures in the block, which further extend the receptive field of each vertex in graphs, learning better sentence representations. Experiments show that our model outperforms existing models on several text similarity datasets, while also verify that GCNs-based text representation models can be trained in a deeper manner, rather than being trained in two or three layers.

Radical Counter Network for Robust Chinese Character Recognition

Yunqing Li, Yixing Zhu, Jun Du, Changjie Wu, Jianshu Zhang

Responsive image

Auto-TLDR; Radical Counter Network for Chinese Character Recognition

Slides Poster Similar

Chinese character recognition has attracted much interest due to its high challenge and various applications. The whole-character modeling method can recognize common characters well but unable to handle unseen situation. Some radical-based modeling methods have successfully achieved great performance in unseen condition but the decoding takes huge time comsumption. Therefore, a high-efficient model which can recognize unseen characters needs to be proposed. First, this paper introduces a novel radical counter network (RCN) to recognize Chinese characters by identifying radicals and spatial structures. The proposed RCN first extracts visual features from input by employing DenseNet as encoder. Then a decoder based on fully connected layer is employed, aiming at synchronously estimating the number of each caption in character. The manner of simultaneously decoding all the captions greatly saves time of sequence decoding. Additionally, we design a multi-task learning to combine global feature extraction capability of whole-character modeling and local feature extraction capability of radical-based modeling, which further improves the model generalization. Experiments on natural scene character dataset demonstrate that the proposed model significantly outperforms baseline by 4.81\% with a comparable model complexity. That shows great robustness and simplicity of our model.

Boundary-Aware Graph Convolution for Semantic Segmentation

Hanzhe Hu, Jinshi Cui, Jinshi Hongbin Zha

Responsive image

Auto-TLDR; Boundary-Aware Graph Convolution for Semantic Segmentation

Slides Poster Similar

Recent works have made great progress in semantic segmentation by exploiting contextual information in a local or global manner with dilated convolutions, pyramid pooling or self-attention mechanism. However, few works have focused on harvesting boundary information to improve the segmentation performance. In order to enhance the feature similarity within the object and keep discrimination from other objects, we propose a boundary-aware graph convolution (BGC) module to propagate features within the object. The graph reasoning is performed among pixels of the same object apart from the boundary pixels. Based on the proposed BGC module, we further introduce the Boundary-aware Graph Convolution Network(BGCNet), which consists of two main components including a basic segmentation network and the BGC module, forming a coarse-to-fine paradigm. Specifically, the BGC module takes the coarse segmentation feature map as node features and boundary prediction to guide graph construction. After graph convolution, the reasoned feature and the input feature are fused together to get the refined feature, producing the refined segmentation result. We conduct extensive experiments on three popular semantic segmentation benchmarks including Cityscapes, PASCAL VOC 2012 and COCO Stuff, and achieve state-of-the-art performance on all three benchmarks.

RLST: A Reinforcement Learning Approach to Scene Text Detection Refinement

Xuan Peng, Zheng Huang, Kai Chen, Jie Guo, Weidong Qiu

Responsive image

Auto-TLDR; Saccadic Eye Movements and Peripheral Vision for Scene Text Detection using Reinforcement Learning

Slides Poster Similar

Within the research of scene text detection, some previous work has already achieved significant accuracy and efficiency. However, most of the work was generally done without considering about the implicit relationship between detection and eye movements. In this paper, we propose a new method for scene text detection especially for its refinement based on reinforcement learning. The idea of this method is inspired by Saccadic Eye Movements and Peripheral Vision. A saccade makes it possible for humans to orient the gaze to the location where a visual object has appeared. Peripheral vision gathers visual information of surroundings which provides supplement to foveal vision during gazing. We propose a simple pipeline, imitating the way human eyes do a saccade and collect peripheral information, to locate scene text roughly and to refine multi-scale vision field iteratively using reinforcement learning. For both training and evaluation, we use ICDAR2015 Challenge 4 dataset as a base and design several criteria to measure the feasibility of our work.